TRAJECTORY TRACKING CONTROL FOR AN UNCERTAIN MOBILE MANIPULATOR: COMBINING SLIDING MODE AND NEURAL NETWORK

2013 ◽  
Vol 33 (4) ◽  
Author(s):  
Meng-Bi Cheng ◽  
Wu-Chung Su ◽  
Ching-Chih Tsai
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Mobile Manipulator ◽  
Trajectory Tracking Control

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

2017 ◽  
Vol 22 (S3) ◽  
pp. 5799-5809 ◽  
Author(s):  
Fei Wang ◽  
Zhi-qiang Chao ◽  
Lian-bing Huang ◽  
Hua-ying Li ◽  
Chuan-qing Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Trajectory Tracking Control ◽  
Fuzzy Sliding Mode

Trajectory Tracking Control of Robot via a Fuzzy Sliding-Mode Controller

2012 ◽  
Vol 433-440 ◽  
pp. 4154-4158
Author(s):  
Hong Mei
Keyword(s):  
Fuzzy Logic ◽  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Logic Controller ◽  
Sliding Mode ◽  
Mobile Manipulator ◽  
Trajectory Tracking Control ◽  
Mode Control ◽  
Fuzzy Logic Approach

This paper presents a new strategy for the trajectory tracking control of robot, using a fuzzy logic approach and sliding mode control. The key properties of sliding mode control are robustness and chattering which are greatly affected by the motion quality of the reaching phase. The reach law is taken to determine the control law which can improve the convergence speed in the reaching phase and impair the chattering.A fuzzy logic controller is taken to adjust the parameters of the reach law timely which makes the system have a high and rational reaching speed during the whole reaching phase. The error convergencing speed is enhanced which boosted the robustness of system indirectly. And the reaching speed is reduced enough to impair the chattering when the system is very near the sliding mode surface. At last, both the robustness and chattering are improved. A mobile manipulator with two arms is taken as an example to track a given trajectory with the proposed controller. It is found that the results are very encouraging.

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